A Novel Discrete DGM (2, 1, t 2 ) Model Based on Stochastic Oscillation Sequence and Its Application

Abstract

This article aims to improve the prediction accuracy of the grey model for stochastic oscillation sequence, to overcome the defect of the direct span from difference to differential and to better depict the growth trend of data series. Firstly, the accelerated translation smoothing transformation is constructed, which transforms the stochastic oscillation sequence into the monotonic increasing sequence that is suitable for modeling. Secondly, the quadratic time power term is introduced to establish a novel discrete DGM (2, 1, $t^2$ ) model, then derive a novel simulation prediction formula, and analyze properties of this novel model; it was concluded that its application scope is expanded. Finally, a novel discrete DGM (2, 1, $t^2$ ) model based on a stochastic oscillation sequence is established to forecast a group of stochastic oscillation sequences with different amplitudes and two different city traffic flow series and compare the prediction accuracy with other grey models; the results show that the feasibility of this model is verified and has good simulated prediction effect in the field of traffic flow.